Predicting Influenza A Virus Infection in the Lung from Hematological Data with Machine Learning
Suneet Singh Jhutty, Julia D. Boehme, Andreas Jeron, Julia Volckmar, Kristin Schultz, Jens Schreiber, Klaus Schughart, Kai Zhou, Jan Steinheimer, H. Stöcker, Sabine Stegemann‐Koniszewski, Dunja Bruder, Esteban A. Hernandez‐Vargas
Abstract
During the course of respiratory infections such as influenza, we do have a very limited view of immunological indicators to objectively and quantitatively evaluate the outcome of a host. Methods for monitoring immunological markers in a host's lungs are invasive and expensive, and some of them are not feasible to perform. Using machine learning algorithms, we show for the first time that minimally invasively acquired hematological parameters can be used to infer lung viral burden, leukocytes, and cytokines following influenza virus infection in mice. The potential of the framework proposed here consists of a new qualitative vision of the disease processes in the lung compartment as a noninvasive tool.